A tree-based clear sky model for DNI forecasting

Abstract

Solar irradiance is a renewable energy that has been utilized by power plants. Direct normal irradiance (DNI) forecasting is a crucial approach to reduce the output uncertainty of solar power plants, and enhance reliability of power grids. According to statistics, the solar radiation of clear sky accounts for more than half of the total amount of radiation in a year. Hence, clear sky DNI forecasting models play a significant role in solar power systems. The existing clear sky models assume that the atmosphere is at rest and ignore the variation of aerosol optical depth in a day, which may reduce the accuracy of DNI forecasting. In this paper, a novel tree-based piecewise clear sky model is proposed to capture aerosol extinction coefficient varying with time in a day and forecast DNI when clouds are absent. Firstly, a linear model is deduced from a physical equation to represent the relationship of air mass and DNI approximately. Then, model trees with this linear mode at each leaf node are create. The database of the National Renewable Energy Laboratory (NREL) is used for experiments to evaluate the performance of the proposed model, compared with other clear sky models. The results of the experiments show that the proposed model provides more accurate forecast of DNI than other clear sky models.